Reads Per Kilobase Million

Calculate reads per kilobase of gene per million mapped reads.

Description

RPKM divides feature read counts by feature kilobases and library millions.

RPKM divides feature read counts by feature kilobases and library millions. Unlike TPM, the resulting feature values do not inherently sum to one million.^[1]

Calculate reads per kilobase of gene per million mapped reads.

When to use Reads Per Kilobase Million

Use this tool to calculate reads per kilobase of gene per million mapped reads.

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.

How to use Reads Per Kilobase Million and understand its results

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.
Enter readCounts, geneLengthsBp, mappedReads using the field table and calculation rules, then run the tool.
Read rpkm, sumRpkm. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
readCounts	Read counts	list of number	Required
geneLengthsBp	Gene lengths bp	list of number	Required
mappedReads	Mapped reads	number	Required

Enter aligned nonempty nonnegative read counts and positive gene lengths in bp with a positive mapped-read total. Return per-feature RPKM and their sum. An all-zero feature list is accepted and returns zeros.

RPKM_i=10⁹ readCount_i/(mappedReads×lengthBp_i)

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
rpkm	Rpkm	list of number
sumRpkm	Sum rpkm	number

json

{
  "input": {
    "readCounts": [
      10,
      20
    ],
    "geneLengthsBp": [
      1000,
      2000
    ],
    "mappedReads": 1000000
  },
  "output": {
    "rpkm": [
      10,
      10
    ],
    "sumRpkm": 20
  }
}

Limitations and assumptions

Lengths, assignment and the denominator are caller inputs. This does not estimate expression uncertainty, normalize compositional changes or treat paired-end fragments as reads automatically.

Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Fragments Per Kilobase Million: Calculate fragments per kilobase of transcript per million mapped fragments. For a complementary task, use Transcripts Per Million: Normalize transcript length-adjusted abundances to one million TPM.

References

Reads Per Kilobase Million — tool.io

Similar or alternative tools

Fragments Per Kilobase Million
Calculate fragments per kilobase of transcript per million mapped fragments.
Transcripts Per Million
Normalize transcript length-adjusted abundances to one million TPM.